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Using Wavelet Neural Network to Analyze the Data of Wet Gas Flowmeter

Author: XiaoGuangYun
Tutor: ZhengJinWu;GengYanFeng
School: China University of Petroleum
Course: Signal and Information Processing
Keywords: Wavelet Neural Network Continuous wavelet transform Multi-resolution analysis Condensate Natural Gas Wavelet basis
CLC: TP216
Type: Master's thesis
Year: 2008
Downloads: 54
Quote: 0
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Abstract


Gas-liquid two-phase flow of gas condensate monitoring , control the main basis of the dynamic characteristics of the gas wells and gas reservoir interference by many factors , but the traditional condensate gas flow measurement error , the gas wells wellhead phase measurement issues is a not been completely resolved technical problems . The paper examines the application of wavelet neural network processing condensate natural gas measurement data . The first systematic study of the structure of wavelet neural network , and compare the performance of the wavelet neural network and the traditional BP network . Then build a different form of wavelet neural network : a continuous wavelet transform as the theoretical basis , using a continuous wavelet function as the activation function of the neural network to build a continuous parameter wavelet neural network , given the parameters learning algorithm ; multiresolution analysis and orthogonal wavelet decomposition of the theoretical basis for using orthogonal wavelet function and scaling function together as a neural network hidden layer activation function , build multi-resolution wavelet neural network , and take advantage of multi-resolution analysis of layer-by-layer approximation of nature , given stratified hierarchical learning algorithm . Application of two network on condensate gas flow meter measurement data processing . Finally, the combination of the measurement signal through the first analysis of the nature of comprehensive error comparison method , compared wavelet basis , and select the best one . The parameters of pressure , temperature and differential pressure as the wavelet network input , gas-liquid standard volume flow output for the network , based on Matlab language , the above methods , the programming , the experimental results show that the wavelet neural network prediction of condensate gas flowmeter flow method is feasible and can better predict the amount of gas-liquid two-phase flow , for the method is applied to actual production laid the foundation .

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